The development of analytic decision applications tends to a time and resource intensive endeavor. An analytic decision application is typically developed by analytic scientists in a familiar language that is optimized for mathematical modeling. Thereafter, the application is rewritten in an enterprise language (e.g., JAVA) in order to integrate with outside security systems as well as to enable monitoring, reporting, and data access. In addition, the application must be configured for deployment and integration with pre-existing information technology (IT) infrastructure. This conventional development process restricts the application to a specific deployment environment while deployment to a different environment will require a repeat of the entire process. After full deployment and running of the application, captured operational and decision metrics, which allow for improvement of the application, have to be manually propagated back to the analytic scientists that originally developed the technology. As such, conventional analytic decision applications lack cross-platform compatibility and are therefore prone to vendor lock-in. Furthermore, data that is generated by conventional analytic decision applications must be transferred back separately.